Shape Description and Recognition Method Inspired by the Primary Visual Cortex

The shape or contour of an object is usually stable and persistent, so it is a good basis for invariant recognition. Before this can be accomplished, two problems must be handled. The first is obtaining clean edges, and the other is organizing those edges into a structured form so they can be manipulated easily. Simple cells in the primary visual cortex are specialized for orientation detection. This neural mechanism can be stimulated using an array model. This model can produce a fairly clean set of lines, all in the form of vectors instead of pixels. These multiple orientation layers are then disintegrated from the original array, and a hierarchical partition tree can be created to re-organize them. Based on the similarity of the trees, a rough classification of objects can be realized. To enable maximum recognition, a moment-based measurement method was designed to describe the layout of active simple cells in each layer in detail. Then, a decision tree was produced using samples re-described using Hu’s moment invariants. This system takes into account more geometric information during the recognition process. The experimental results suggest that the representation efficiency enabled by simple cells, and their neural mechanism and the application of a multi-layered representation schema can simplify the algorithm. This further demonstrates the crucial role of simple cells in the visual processing path and shows that they can facilitate subsequent shape processing.

[1]  A Shmuel,et al.  Coexistence of linear zones and pinwheels within orientation maps in cat visual cortex. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Scott O. Murray,et al.  Perceptual grouping and the interactions between visual cortical areas , 2004, Neural Networks.

[3]  Klaus Schulten,et al.  Models of Orientation and Ocular Dominance Columns in the Visual Cortex: A Critical Comparison , 1995, Neural Computation.

[4]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[5]  Chun-I Yeh,et al.  On and off domains of geniculate afferents in cat primary visual cortex , 2008, Nature Neuroscience.

[6]  Tetsuya Yagi,et al.  Neuromorphic VLSI vision system for real-time texture segregation , 2008, Neural Networks.

[7]  G. Blasdel,et al.  Voltage-sensitive dyes reveal a modular organization in monkey striate cortex , 1986, Nature.

[8]  Andrea Torsello,et al.  Four metrics for efficiently comparing attributed trees , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[9]  Risto Miikkulainen,et al.  Computational Maps in the Visual Cortex , 2005 .

[10]  Philip Bille,et al.  A survey on tree edit distance and related problems , 2005, Theor. Comput. Sci..

[11]  Mauro Ursino,et al.  A model of contour extraction including multiple scales, flexible inhibition and attention , 2008, Neural Networks.

[12]  Marc-Oliver Gewaltig,et al.  Simulations of signal flow in a functional model of the cortical column , 2007, Neurocomputing.

[13]  Tetsuya Yagi,et al.  Neuromorphic binocular vision system for real-time disparity estimation , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[14]  Hui Wei,et al.  An Orientation Detection Model Based on Fitting from Multiple Local Hypotheses , 2012, ICONIP.

[15]  Dietmar Heinke,et al.  Modelling Visual Search with the Selective Attention for Identification Model (VS-SAIM): A Novel Explanation for Visual Search Asymmetries , 2010, Cognitive Computation.

[16]  Atsushi Shimada,et al.  Visual feature extraction using variable map-dimension Hypercolumn Model , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[17]  Andrea Vedaldi,et al.  Vlfeat: an open and portable library of computer vision algorithms , 2010, ACM Multimedia.

[18]  N. Swindale.  The development of topography in the visual cortex: a review of models. , 1996, Network.

[19]  Nathalie Guyader,et al.  Improving Visual Saliency by Adding ‘Face Feature Map’ and ‘Center Bias’ , 2012, Cognitive Computation.

[20]  Hui Wei,et al.  A Mathematical Model of Retinal Ganglion Cells and Its Applications in Image Representation , 2013, Neural Processing Letters.

[21]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[22]  Risto Miikkulainen,et al.  Self-organization of hierarchical visual maps with feedback connections , 2006, Neurocomputing.

[23]  Risto Miikkulainen,et al.  Joint maps for orientation, eye, and direction preference in a self-organizing model of V1 , 2006, Neurocomputing.

[24]  Nathalie Guyader,et al.  A Functional and Statistical Bottom-Up Saliency Model to Reveal the Relative Contributions of Low-Level Visual Guiding Factors , 2010, Cognitive Computation.

[25]  Hui Wei,et al.  A New Model to Simulate the Formation of Orientation Columns Map in Visual Cortex , 2011, ISNN.

[26]  Basabi Bhaumik,et al.  Study of spatial frequency selectivity and its spatial organization in the visual cortex through a feedforward model , 2005, Neurocomputing.

[27]  Hui Wei,et al.  The Depth Estimate of Interesting Points from Monocular Vision , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[28]  Hui Wei,et al.  A group-decision making model of orientation detection , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[29]  Tarek M. Taha,et al.  A preliminary investigation of a neocortex model implementation on the Cray XD1 , 2007, Proceedings of the 2007 ACM/IEEE Conference on Supercomputing (SC '07).

[30]  Baran Çürüklü,et al.  A model of the summation pools within the layer 4 (area 17) , 2005, Neurocomputing.

[31]  D. Fitzpatrick,et al.  Spatial coding of position and orientation in primary visual cortex , 2002, Nature Neuroscience.

[32]  D. Hubel,et al.  Sequence regularity and geometry of orientation columns in the monkey striate cortex , 1974, The Journal of comparative neurology.

[33]  Bertram E. Shi,et al.  Integrating contrast invariance into a model for cortical orientation map formation , 2009, Neurocomputing.